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Re: st: fixed effect or random effect model
From
John Antonakis <[email protected]>
To
[email protected]
Subject
Re: st: fixed effect or random effect model
Date
Sun, 06 May 2012 10:31:21 +0200
It would be more correct to say that if the p-value for the Hausman
test, where you compare random vs fixed-effects, is < .05 then the
random-effects estimator is no good (i.e., the test is in the form
"hausman fe re"). The fixed-effects estimator is consistent; however,
the random-effects estimator is more efficient. If the estimates using
random effects are not significantly different from the fixed-effects
estimator (i.e., the p-value is > .05) then you can retain the
random-effects estimator.
In your case, it would be best to use the user-written -xtoverid- test
(available from SSC) after having run
xtreg cost duration sex age group, re cluster(id_indicator)
(id_indicator is your panel identifier)
The xtoverid test accommodates a cluster robust xtreg vce. Specifically,
it is a Hausman-type test that constrains the covariance between uj (the
fixed-effect) and the regressors to zero. See "help xtoverid": here is
the relevant extract from the help file:
"A test of fixed vs. random effects can also be seen as a test of
overidentifying
restrictions. The fixed effects estimator uses the orthogonality
conditions that the
regressors are uncorrelated with the idiosyncratic error e_it, i.e.,
E(X_it*e_it)=0.
The random effects estimator uses the additional orthogonality
conditions that the
regressors are uncorrelated with the group-specific error u_i (the
"random effect"),
i.e., E(X_it*u_i)=0. These additional orthogonality conditions are
overidentifying
restrictions. The test is implemented by xtoverid using the artificial
regression
approach described by Arellano (1993) and Wooldridge (2002, pp. 290-91),
in which a
random effects equation is reestimated augmented with additional
variables consisting of
the original regressors transformed into deviations-from-mean form. The
test statistic
is a Wald test of the significance of these additional regressors. A
large-sample
chi-squared test statistic is reported with no degrees-of-freedom
corrections. Under
conditional homoskedasticity, this test statistic is asymptotically
equivalent to the
usual Hausman fixed-vs-random effects test; with a balanced panel, the
artificial
regression and Hausman test statistics are numerically equal. See
Arellano (1993) for
an exact statement and the example below for a demonstration. Unlike
the Hausman
version, the test reported by xtoverid extends straightforwardly to
heteroskedastic- and
cluster-robust versions, and is guaranteed always to generate a
nonnegative test
statistic."
HTH,
J.
__________________________________________
Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 06.05.2012 02:29, [email protected] wrote:
The Hausman test is actually use to select between fixed and random effect. To know which one to chose you proceed as follow: if the p value is greater than 0.5 then the fixed effect(fe ) is not good chose random effect(re ) and otherwise if reverse is the case. Secondly, to test for autocorrelation after the. 'xtreg' test, you use 'xttest0'
Sent from my BlackBerry wireless device from MTN
-----Original Message-----
From: Caliph Omar Moumin<[email protected]>
Sender: [email protected]
Date: Sat, 5 May 2012 07:46:33
To: [email protected]<[email protected]>
Reply-To: [email protected]
Subject: st: fixed effect or random effect model
Dear all
For the past two weeks i spent to decide whether i apply fixed effect or random effect model in my strongly unbalanced panel data. But I couldn't decide it.
These are the tests i applied so could you please give a minute and advice me what to apply? I understood the my hausman test impllies that i can apply either fixed or random effect modells. Is that so? If that is correct then i choose to apply the random effect model becuase of some time in-variant involved.
What about Breusch-Pagan Lagrange multiplier (LM) test? I have no clue as to how interperate this test? Could any help me?
xtdescribe
id: 6, 9, ..., 809378 n = 14503
nadmission1: 1, 2, ..., 16 T = 16
Delta(nadmission1) = 1 unit
Span(nadmission1) = 16 periods
(id*nadmission1 uniquely identifies each observation)
Distribution of T_i: min 5% 25% 50% 75% 95% max
1 1 1 1 1 2 16
Freq. Percent Cum. | Pattern
---------------------------+------------------
13302 91.72 91.72 | 1...............
797 5.50 97.21 | 11..............
160 1.10 98.32 | 111.............
97 0.67 98.99 | 1111............
58 0.40 99.39 | 11111...........
31 0.21 99.60 | 111111..........
29 0.20 99.80 | 1111111.........
12 0.08 99.88 | 11111111........
8 0.06 99.94 | 111111111.......
9 0.06 100.00 | (other patterns)
---------------------------+------------------
14503 100.00 | XXXXXXXXXXXXXXXX
I want to compare between this two groups
xttab group;
Overall Between Within
group | Freq. Percent Freq. Percent Percent
----------+-----------------------------------------------------
alcohol | 275 1.64 191 1.32 100.00
nonalcoh | 16443 98.36 14312 98.68 100.00
----------+-----------------------------------------------------
Total | 16718 100.00 14503 100.00 100.00
(n = 14503)
.quietly xtreg cost duration sex age group, fe;
. estimates store fixed;
. quietly xtreg cost duration sex age group, re;
. estimates store random;
hausman fixed random;
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
-------------+----------------------------------------------------------------
duration | 874.4642 944.5754 -70.11117 84.24204
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 0.69
Prob>chi2 = 0.4053
Breusch-Pagan Lagrange multiplier (LM)test is performed as follows
xtreg cost duration, re;
xttest0;
Breusch and Pagan Lagrangian multiplier test for random effects
cost[id,t] = Xb + u[id] + e[id,t]
Estimated results:
| Var sd = sqrt(Var)
---------+-----------------------------
cost | 2.27e+09 47647.13
e | 6.78e+08 26038.66
u | 1.66e+09 40752.23
Test: Var(u) = 0
chi2(1) = 59.40
Prob> chi2 = 0.0000
A test for heteroskedasticity is performed which shows presence
xtreg cost duration, fe
xttest3
Modified Wald test for groupwise heteroskedasticity
in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i
chi2 (14503) = 2.1e+36
Prob>chi2 = 0.0000
Kind Regards,
Moumin
Email: [email protected]
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